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| from langchain_huggingface.llms import HuggingFacePipeline | |
| from langchain_core.prompts import PromptTemplate | |
| import gradio as gr | |
| hf = HuggingFacePipeline.from_model_id( | |
| model_id="gpt2", | |
| task="text-generation", | |
| pipeline_kwargs={"max_new_tokens": 100}, | |
| ) | |
| template = """Question: {question} | |
| Answer: Let's think step by step.""" | |
| prompt = PromptTemplate.from_template(template) | |
| chain = prompt | hf | |
| def respond(question,history): | |
| return chain.invoke({"question": question}) | |
| chat_interface = gr.ChatInterface(fn=respond, title="Q&A Chatbot", description="Ask any question and get an answer!") | |
| # Launch the interface | |
| chat_interface.launch() |